Artificial neuron technology is emerging as a viable design alternative for many applications including communications and control. As practical commercial components become increasingly available, solutions to signalling and control problems utilizing these components will be needed.
Remote signalling and/or switching has basic utility in a broad spectrum of application. The current invention is for a novel system using an elementary form of the McCullough-Pitts (MP) neuron for encoding a selector switch position for transmission to a remote location where the received message is decoded so that the same selector position is assumed at the receiver site. The decoder at the receiver is another artificial neural network comprised of known or standard neural network components.
In an article entitled "How to Solve the N Bit Encoder Problem with Just Two Hidden Units" by Leonid Kruglyak in Neural Computation 2, 399-401 (1990), Massachusetts Institute of Technology, Kruglyak implies that a three layer network of the standard feedforward architecture can solve the N input-N output encoder problem with just two hidden units. This structure is i sometimes referred to as a N-2-N encoder/decoder network. Other known prior art required a N-log.sub.2 N-N network with hidden Log.sub.2 N units to solve the problem, as reported in "Parallel Distributed Processing: Explorations in the Microstructures of Cognition", Rumelhart, D. E. and McClelland, J. L., eds., Vol. 1, Chap. 8, p. 318, MIT Press, Cambridge, Mass.
FIG. 1 shows the structure implied by Kruglyak consisting of encoder unit 10 and decoder unit 20. The encoder unit 10 consists of a N-position slider switch with wiper arm 11 and contacts 13. Wiper arm 11 is connected to a constant voltage source 9 which may, at any time, be applied to only one contact 13 out of a total of N contacts. Each contact 13 is connected to neural processing elements (PE) 17 and 19 by synaptic weighting elements 15. Signals on output lines 14 from PE 17 and 19, represents the encoded switch position (1 through N) of wiper arm 11.
FIG. 2 shows a typical MP neuron and its relationship to the encoder structure 10 of FIG. 1. The inputs (1-N) are applied to input nodes 13 that are each connected to a PE unit by synaptic weighting elements 15 (W1, W2, . . . ,WN). Summing unit 12 adds these weighted inputs together with an appropriate threshold offset 0. The output of summing unit 12 is applied to an output (squashing) nonlinearity 16. Nonlinearity 16 typically has a sigmoidal transfer characteristic. In this manner, PE elements 17 and 19 encode the state of selector switch arm 11 for transmission over output lines 14.
Neuron decoder unit 20 of FIG. 1 consists of N PE 23 units (PE1-N) each having two synaptic connections 21, one to each line of the input line pair 14. The output of the PE 23 units constitutes a N-terminal output array. Upon proper selection of encoder 10 and decoder 20 synaptic weights, output terminals of unit 20 may all be active at various voltage levels. However, the output voltage of the PE unit, PE.sub.j, corresponding to the selected jth input active terminal 13, is maximum relative to the other N-1 output terminals.
Although the output of decoder network 20 contains all of the necessary information required to determine which one-out-of-N inputs was activated, it is not fully decoded in the sense that all output terminals 25 will have analog voltages that are representative of their "closeness" to the selected jth input terminal 13 of encoder 10 Additional processing would be required in order to select the jth output terminal 25 having the largest output voltage. A winner-take-all (WTA) network which may be used for this purpose, accepts the N output terminals 25 as its input and activates the one-out-of-N of its output terminals corresponding to the input terminal having the maximum voltage value.
Many possible implementations for the WTA network are known to those skilled in the art. For example, the MAXNET neural network described in the book "Adaptive Pattern Recognition and Neural Networks" by Yoh-Han Pao, section 7.2, p. 173, Addison-Wesley Publishing Company, Inc., 1989. MAXNET uses lateral inhibition to accentuate and select one candidate node, among a set of nodes, that has the greatest output." It is described as a "feedforward Hamming net maximum - likelihood classifier of patterns of binary inputs, corrupted by noise." Although the input patterns in this application are analog voltages, the property of the network is to select the input with the largest magnitude, treating each input as a possible one (on-state) and selecting the largest valued input as the most likely on-state candidate.
Thus, prior art teaches that an N-state encoder/decoder system such as shown in FIG. 1, requires that transmission line unit 14 be comprised of at least two distinct lines. In general, it would be advantageous to reduce the number of transmission channels required particularly if the reduction is achievable at no, or relatively low, cost.